1 code implementation • 25 Oct 2023 • Cong Wang, Xiaofeng Cao, Lanzhe Guo2, Zenglin Shi
In this paper, we propose a novel SSL method called DualMatch, in which the class prediction jointly invokes feature embedding in a dual-level interaction manner.
1 code implementation • 30 Jun 2023 • Zenglin Shi, Ying Sun, Mengmi Zhang
However, the vanilla mask generation method of SAM lacks class-specific information in the masks, resulting in inferior counting accuracy.
Ranked #1 on Object Counting on PASCAL VOC
no code implementations • 8 Jun 2023 • Zenglin Shi, Pascal Mettes, Cees G. M. Snoek
Where density-based counting methods typically use the point annotations only to create Gaussian-density maps, which act as the supervision signal, the starting point of this work is that point annotations have counting potential beyond density map generation.
no code implementations • 23 Nov 2022 • Xiao Liu, Ankur Sikarwar, Gabriel Kreiman, Zenglin Shi, Mengmi Zhang
To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts.
no code implementations • 21 Nov 2022 • Zenglin Shi, Ying Sun, Joo Hwee Lim, Mengmi Zhang
To the best of our knowledge, no existing technique can accomplish all of these objectives simultaneously.
1 code implementation • ICCV 2021 • Shuo Chen, Zenglin Shi, Pascal Mettes, Cees G. M. Snoek
We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.
Ranked #1 on Video Visual Relation Detection on VidOR
1 code implementation • 19 Jul 2021 • Zenglin Shi, Pascal Mettes, Guoyan Zheng, Cees Snoek
In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images.
1 code implementation • 2 Jul 2021 • Zenglin Shi, Pascal Mettes, Subhransu Maji, Cees G. M. Snoek
The deep image prior showed that a randomly initialized network with a suitable architecture can be trained to solve inverse imaging problems by simply optimizing it's parameters to reconstruct a single degraded image.
1 code implementation • 2 Jun 2021 • Zenglin Shi, Yunlu Chen, Efstratios Gavves, Pascal Mettes, Cees G. M. Snoek
The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.
no code implementations • 24 Dec 2019 • Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
no code implementations • 24 Aug 2019 • Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
1 code implementation • ICCV 2019 • Zenglin Shi, Pascal Mettes, Cees G. M. Snoek
To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations.
no code implementations • 21 Feb 2019 • Xiahai Zhuang, Lei LI, Christian Payer, Darko Stern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Orjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, Guang Yang
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
1 code implementation • CVPR 2018 • Zenglin Shi, Le Zhang, Yun Liu, Xiaofeng Cao, Yangdong Ye, Ming-Ming Cheng, Guoyan Zheng
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
Ranked #9 on Crowd Counting on WorldExpo’10